data-transport/notebooks/mysql.ipynb

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    "#### Writing to MySQL\n",
    "\n",
    "1. Insure MySQL is actually installed on the system, \n",
    "2. There is a database called demo created on the said system\n",
    "\n",
    "The cell below creates a dataframe that will be stored within postgreSQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Writing to PostgreSQL database\n",
    "#\n",
    "import transport\n",
    "from transport import providers\n",
    "import pandas as pd\n",
    "_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
    "myw = transport.factory.instance(provider=providers.MYSQL,database='demo',table='friends',context='write',auth_file=\"/home/steve/auth-mysql.json\")\n",
    "myw.write(_data,if_exists='replace') #-- default is append\n",
    "print (transport.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading from MySQL\n",
    "\n",
    "The cell below reads the data that has been written by the cell above and computes the average age within a MySQL (simple query). \n",
    "\n",
    "- Basic read of the designated table (friends) created above\n",
    "- Execute an aggregate SQL against the table\n",
    "\n",
    "**NOTE**\n",
    "\n",
    "It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           name  age\n",
      "0    James Bond   55\n",
      "1  Steve Rogers  150\n",
      "2  Steve Nyemba   44\n",
      "--------- STATISTICS ------------\n",
      "   _counts   avg\n",
      "0        3  83.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import transport\n",
    "from transport import providers\n",
    "myr = transport.instance(provider=providers.POSTGRESQL,database='demo',table='friends',auth_file='/home/steve/auth-mysql.json')\n",
    "_df = myr.read()\n",
    "_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
    "_sdf = myr.read(sql=_query)\n",
    "print (_df)\n",
    "print ('--------- STATISTICS ------------')\n",
    "print (_sdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    "The auth_file is intended to be **JSON** formatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'host': 'klingon.io',\n",
       " 'port': 3306,\n",
       " 'username': 'me',\n",
       " 'password': 'foobar',\n",
       " 'database': 'demo',\n",
       " 'table': 'friends'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"host\":\"klingon.io\",\"port\":3306,\"username\":\"me\",\"password\":\"foobar\",\n",
    "    \"database\":\"demo\",\"table\":\"friends\"\n",
    "}"
   ]
  }
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